import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 	lowerCamelCase					(	UpperCamelCase     :				int							)   ->    List[Any]:
		_lowerCamelCase				   =  [
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    'decoder.output_projection.weight',
		    '_float_tensor',
		    'encoder.embed_positions._float_tensor',
		    'decoder.embed_positions._float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(UpperCamelCase			,       UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				str							)   ->    Any:
		_lowerCamelCase   ,							_lowerCamelCase				   =  emb.weight.shape
		_lowerCamelCase				   =  nn.Linear(UpperCamelCase			,       UpperCamelCase			,       bias=UpperCamelCase							)
		_lowerCamelCase				   =  emb.weight.data
		return lin_layer


def 	lowerCamelCase					(	UpperCamelCase     :				Dict							)   ->    Optional[Any]:
		_lowerCamelCase				   =  torch.load(UpperCamelCase			,       map_location='cpu'							)
		_lowerCamelCase				   =  mam_aaa['args'] or mam_aaa['cfg']['model']
		_lowerCamelCase				   =  mam_aaa['model']
		remove_ignore_keys_(UpperCamelCase							)
		_lowerCamelCase				   =  state_dict['encoder.embed_tokens.weight'].shape[0]

		_lowerCamelCase				   =  MaMaaaConfig(
		    vocab_size=UpperCamelCase			,       max_position_embeddings=10_24			,       encoder_layers=args.encoder_layers			,       decoder_layers=args.decoder_layers			,       encoder_attention_heads=args.encoder_attention_heads			,       decoder_attention_heads=args.decoder_attention_heads			,       encoder_ffn_dim=args.encoder_ffn_embed_dim			,       decoder_ffn_dim=args.decoder_ffn_embed_dim			,       d_model=args.encoder_embed_dim			,       encoder_layerdrop=args.encoder_layerdrop			,       decoder_layerdrop=args.decoder_layerdrop			,       dropout=args.dropout			,       attention_dropout=args.attention_dropout			,       activation_dropout=args.activation_dropout			,       activation_function='relu'			,       )

		_lowerCamelCase				   =  state_dict['decoder.embed_tokens.weight']
		_lowerCamelCase				   =  MaMaaaForConditionalGeneration(UpperCamelCase							)
		model.model.load_state_dict(UpperCamelCase			,       strict=UpperCamelCase							)
		_lowerCamelCase				   =  make_linear_from_emb(model.model.shared							)

		return model


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
				parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				A						=			parser.parse_args()
				A						=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
				model.save_pretrained(args.pytorch_dump_folder_path)
from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return math.sqrt(sum(pow(a - b			,       2							) for a, b in zip(UpperCamelCase			,       UpperCamelCase							)							)							)


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    list[list[list[float] | float]]:

		if dataset.ndim != value_array.ndim:
				_lowerCamelCase				   =  (
				    'Wrong input data\'s dimensions... '
				    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
				)
				raise ValueError(UpperCamelCase							)

		try:
				if dataset.shape[1] != value_array.shape[1]:
						_lowerCamelCase				   =  (
						    'Wrong input data\'s shape... '
						    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
						)
						raise ValueError(UpperCamelCase							)
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError('Wrong shape'							)

		if dataset.dtype != value_array.dtype:
				_lowerCamelCase				   =  (
				    'Input data have different datatype... '
				    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
				)
				raise TypeError(UpperCamelCase							)

		_lowerCamelCase				   =  []

		for value in value_array:
				_lowerCamelCase				   =  euclidean(UpperCamelCase			,       dataset[0]							)
				_lowerCamelCase				   =  dataset[0].tolist()

				for dataset_value in dataset[1:]:
						_lowerCamelCase				   =  euclidean(UpperCamelCase			,       UpperCamelCase							)

						if dist > temp_dist:
								_lowerCamelCase				   =  temp_dist
								_lowerCamelCase				   =  dataset_value.tolist()

				answer.append([vector, dist]							)

		return answer


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return np.dot(UpperCamelCase			,       UpperCamelCase							) / (norm(UpperCamelCase							) * norm(UpperCamelCase							))


if __name__ == "__main__":
				import doctest

				doctest.testmod()